To train the deep networks, several loss functions with desirable properties have been formulated, which enable the neural network to learn the mapping from the data space to the embedding space. Conventional methods, such as contrastive loss [1]}, [2]} which takes tuples with two samples and triplet loss [3]}, [4]} which takes three sample tuples, consider the similarity between few samples. In contrast, methods like lifted structure loss [5]} aim to exploit all the samples present in a batch to learn more informative representations. Other methods [6]}, [7]} that utilize several samples in the loss have also been proposed.
